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juandisay

code-memory-rs

by juandisay

Code-Memory MCP (Rust Version)

Semantic code search for your local repositories, powered by SQLite Vector Search, AST-based chunking, and Parallel AI Cascading.

Find functions, classes, and logic across your codebase using natural language — 100% local, accurate, and resilient.


✨ Key Features

  • 🔍 Hybrid Search (Vector + FTS5) — Fast local semantic retrieval using sqlite-vec combined with Full-Text Search (FTS5) and Reciprocal Rank Fusion (RRF) for maximum accuracy.

  • 📦 AST-based Smart Chunking — Language-aware splitting (Python, JS, TS, Rust, Go, Java, Markdown) that preserves functional context using Tree-sitter.

  • Parallel AI Collaboration (Async Planning) — Support for non-blocking, asynchronous planning tasks that allow Worker and Mahaguru (planner) models to work in parallel.

  • 🔄 Background Indexing — High-performance, non-blocking indexing of large folders with job monitoring and status tracking.

  • 🧠 Session-based Chat Context — Automatically preserves and restores chat summaries per project to maintain context across sessions.

  • 📏 Agent Rules Sync — Synchronizes specialized Antigravity/Cursor rules based on the detected project stack.

  • 🛡️ Path Sandboxing — Secure file access with environment-based path validation.

  • 🔌 MCP Protocol — Native integration as an MCP server for Claude Desktop, Cursor, Antigravity, and more.


Related MCP server: Codebase Contextifier 9000

🔌 MCP Tools Overview

The server exposes 13 powerful tools for AI agents to interact with your codebase:

Tool

Description

semantic_code_search

Hybrid search (Vector + FTS5 + RRF) with heuristic re-ranking.

index_folder

Initiates background indexing for a local project folder.

get_index_stats

Monitors active indexing and planning (Mahaguru) jobs.

list_indexed_projects

Lists all projects currently available in the index.

delete_project

Removes a project and its chat context from the index.

request_mahaguru_refinement

Synchronous escalation to a high-level planner (Mahaguru) model.

request_async_mahaguru_refinement

Non-blocking escalation; returns a Job ID for parallel workflows.

get_planning_job_result

Polls the result of an asynchronous planning/refinement job.

save_project_chat_context

Persists a summary of the current session context.

get_project_chat_context

Retrieves the last saved session context for a project.

sync_agent_rules

Updates .agents/rules based on the project's detected stack.

maintenance_prune

Removes stale entries for files that no longer exist on disk.

rebuild_index_database

Factory Reset: Clears the entire database (use with caution).


🚀 Quick Start

1. Requirements

  • Rust 1.80+ (Stable toolchain via rustup).

  • sqlite-vec (Binary vec0.dylib for macOS or equivalent for your OS must be in the binary directory).

2. Build

git clone <your-repo-url> mcp-code-search
cd mcp-code-search
cargo build --release

3. Run

Mode A: MCP Server (Stdin/Stdout) Best for Claude Desktop, Cursor, and other IDE integrations.

cargo run --release -- --mcp

Mode B: HTTP Management API Runs a REST API (default on port 8000) for managing the server.

cargo run --release -- --port 8000

Mode C: CLI Indexer One-off indexing without running a server.

cargo run --release -- --index /path/to/project

🔌 IDE & Client Integration

Claude Desktop / Cursor / Antigravity

Add the following configuration to your MCP settings. Using the provided run_mcp.sh is highly recommended as it ensures the binary is built and run from the correct root with all environment variables.

macOS / Linux

{
  "mcpServers": {
    "code-memory-rs": {
      "command": "/absolute/path/to/mcp-code-search-rs/run_mcp.sh"
    }
  }
}
TIP

Petunjuk Integrasi (Bahasa Indonesia): Gunakan file run_mcp.sh untuk memastikan server berjalan stabil di Claude/Cursor. Skrip ini akan otomatis menjalankan cargo build jika ada perubahan kode, memastikan binary terbaru selalu digunakan. Pastikan path yang Anda masukkan adalah path absolut.


🏗️ Architecture

  • Engine: Pure Rust with tokio for async orchestration.

  • Storage: SQLite (WAL mode) for reliable persistence.

  • Search: sqlite-vec for vector embeddings + FTS5 for keyword matching, merged via Reciprocal Rank Fusion (RRF).

  • Chunking: AST-aware logic using tree-sitter to preserve logical boundaries (functions, classes).

  • Hardening: State-machine based balanced-brace JSON extraction for reliable processing of unpredictable AI outputs.

  • Safety: Environment-variable driven path sandboxing (ALLOWED_PATHS).

  • Observability: Integrated tracing spans and instrumented futures for deep visibility into async/sync execution boundaries.

  • Resilience: Robust API retry logic with exponential backoff and circuit breaking for all LLM interactions.


📁 Project Structure

mcp-code-search/
├── src/                 # Rust source (Core, API, Indexer)
├── data/                # Database and persistent state
├── docs/                # Extended documentation suite
├── bin/                 # Helper scripts and utilities
└── Cargo.toml           # Package metadata

📄 License

MIT

F
license - not found
-
quality - not tested
C
maintenance

Maintenance

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Releases (12mo)
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